{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "30e67d02-4f75-4f15-8b99-1f74e4714731",
   "metadata": {},
   "source": [
    "## 把下面例子改为tanh函数，运行并画图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7f74e861",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "plt.figure()\n",
    "plt.axis([-10,10,-1.5,1.5])\n",
    "plt.grid(True)\n",
    "X=np.arange(-10,10,0.1)\n",
    "y=np.tanh(X)\n",
    "plt.plot(X,y,'b-')\n",
    "plt.title(\"Tanh function\")\n",
    "# plt.savefig(\"fig-res-logstic_fuction.pdf\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be5aa5ce",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
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